A good eye tracker calibration is essential if we want to get high quality gaze data.
But having the user undergo a lengthy calibration procedure
before using the system is not always desirable,
and the quality of the gaze recordings after calibration is often still not sufficient.
We therefore investigated the question
of how to recalibrate gaze data that has already been collected.
Traditional five or nine point eye tracker calibration would
be too obtrusive in a real-world setting.
We therefore want to calibrate the gaze data
after it has been collected with a ready-to-use eye tracker.
Using the data of 26 real customers of a burger restaurant,
we evaluated four different recalibration strategies.
Since we collected the gaze data in an actual purchase situation from authentic customers
we could not add a validation procedure to assess the quality of the collected gaze data.
Therefore, in order to be able to judge the effectiveness of the calibration strategies
we needed some other benchmark for "good quality" gaze data.
We collected this benchmark data in a controlled laboratory study
with an initial calibration and a subsequent gaze validation.
For the initial calibration we used a pursuits-based approach
where the participants were asked to follow a moving burger icon with their gaze.
They were then asked to put together a meal using
the same order application that we also used in the field study.
After the experiment we evaluated the accuracy of the gaze data
by asking the participants to look at five red circles with known screen positions.
From the resulting data we learned that accurate data
shows a very high accumulation of gaze points
around the interaction buttons
and on the main objects of the interface.
We therefore visually examined the gaze data from the field study after applying each calibration strategy.
When we detected the typical gaze patterns that wenow associated with "high quality" gaze data
we classified the calibration as successful.
The first calibration strategy that we tested is based on the assumption
that users look at interaction elements when they click them.
It therefore matches the gaze with the center of a pressed button.
This effectively improved the collected gaze data.
We then proceeded with a series of saliency based calibration strategies.
Assuming that users look at salient elements first
one strategy is to match the gaze with the center of the largest object on the screen.
Another strategy is to align the recorded gaze
with the center of the object closest to the recorded gaze...
...or with the closest one of the ten percent most salient pixels.
Unfortunately, none of these strategies
led to more accurate gaze data when applied alone.
However, we suspected that calibration based on
the closest large object or individual pixel might be beneficial
if the original data is already relatively accurate.
We therefore applied these two calibration strategies to gaze data
that had already been corrected based on interaction-informed calibration.
This indeed resulted in a more refined calibration
in the case of focused saliency correlation
These qualitative analyses show that the recalibration strategies
can effectively improve the quality of recorded gaze data.
In order to validate our findings we are currently designing a user study
to collect gaze data in a controlled setting
while the users interact with the ordering software.
By then validating the gaze data in a procedure similar to the one applied
in our benchmark study
we can quantitatively assess the effectiveness of each calibration strategy.
We will conduct the experiment under varying environmental conditions,
so that the results can again be extrapolated to a real-world setting.